Use of High Resolution Spatiotemporal Gene Expression Data to Uncover Novel Tissue-Specific Promoters in Tomato

Genetic modification can be an effective strategy for improving the agronomic traits of tomato (Solanum lycopersicum) to meet demands for yield, quality, functional components, and stress tolerance. However, limited numbers of available tissue-specific promoters represent a bottleneck for the design and production of transgenic plants. In the current study, a total of 25 unigenes were collected from an RNA-sequence dataset based on their annotation as being exclusively expressed in five type of tissues of tomato pericarp (outer and inner epidermis, collenchyma, parenchyma, and vascular tissues), and every five unigenes, was respectively selected from each tissue based on transcription expression. The 3-kb 5′ upstream region of each unigene was identified from the tomato genome sequence (SL2.50) using annotated unigene sequences, and the promoter sequences were further analyzed. The results showed an enrichment in T/A (T/A > 70%) in the promoter regions. A total of 15 putative tissue-/organ-specific promoters were identified and analyzed by real-time (RT) quantitative (q) PCR analysis, of which six demonstrated stronger activity than widely used tissue-specific tomato promoters. These results demonstrate how high spatiotemporal and high throughput gene expression data can provide a powerful means of identifying spatially targeted promoters in plants.

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METABOLIC RECONSTRUCTION FOR DEVELOPING A TISSUE-SPECIFIC MODEL FOR CATTLE GLYCOLYSIS USING COBRA

Journal of Biological System ◽

10.1142/s0218339017500127 ◽

2017 ◽

Vol 25 (02) ◽

pp. 231-246

Author(s):

DO GYUN KIM ◽

WANG-HEE LEE ◽

SUNG-WON SEO ◽

HYE-SUN PARK

Keyword(s):

Gene Expression ◽

Mammary Gland ◽

Epithelial Cells ◽

Gene Expression Data ◽

Specific Model ◽

Expression Data ◽

Bovine Mammary Gland ◽

Tissue Specific ◽

Metabolic Reactions ◽

Mammary Gland Epithelial Cells

This study aims at developing a tissue-specific model for glycolysis in bovine mammary gland epithelial cells by incorporating gene expression data into metabolic reactions. Two types of data sets were embedded in the COnstraint-Based Reconstruction Analysis (COBRA) toolbox: metabolic reactions that overlay bovine genetic information into human glycolysis data from a public database and gene expression data of cattle acquired from lab experiments. As a result, we successfully generated a tissue-specific model of bovine glycolysis in bovine mammary gland epithelial cells, providing information on expression of metabolic pathways and gene annotations still required for curation.

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CLUSTERING AND RE-CLUSTERING FOR PATTERN DISCOVERY IN GENE EXPRESSION DATA

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001053 ◽

2005 ◽

Vol 03 (02) ◽

pp. 281-301 ◽

Cited By ~ 3

Author(s):

PATRICK C. H. MA ◽

KEITH C. C. CHAN ◽

DAVID K. Y. CHIU

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Gene Expression Data ◽

Binding Sites ◽

Transcription Factor Binding Sites ◽

Transcription Factor Binding ◽

Pairwise Distance ◽

Expression Data ◽

Promoter Regions ◽

Factor Binding

The combined interpretation of gene expression data and gene sequences is important for the investigation of the intricate relationships of gene expression at the transcription level. The expression data produced by microarray hybridization experiments can lead to the identification of clusters of co-expressed genes that are likely co-regulated by the same regulatory mechanisms. By analyzing the promoter regions of co-expressed genes, the common regulatory patterns characterized by transcription factor binding sites can be revealed. Many clustering algorithms have been used to uncover inherent clusters in gene expression data. In this paper, based on experiments using simulated and real data, we show that the performance of these algorithms could be further improved. For the clustering of expression data typically characterized by a lot of noise, we propose to use a two-phase clustering algorithm consisting of an initial clustering phase and a second re-clustering phase. The proposed algorithm has several desirable features: (i) it utilizes both local and global information by computing both a "local" pairwise distance between two gene expression profiles in Phase 1 and a "global" probabilistic measure of interestingness of cluster patterns in Phase 2, (ii) it distinguishes between relevant and irrelevant expression values when performing re-clustering, and (iii) it makes explicit the patterns discovered in each cluster for possible interpretations. Experimental results show that the proposed algorithm can be an effective algorithm for discovering clusters in the presence of very noisy data. The patterns that are discovered in each cluster are found to be meaningful and statistically significant, and cannot otherwise be easily discovered. Based on these discovered patterns, genes co-expressed under the same experimental conditions and range of expression levels have been identified and evaluated. When identifying regulatory patterns at the promoter regions of the co-expressed genes, we also discovered well-known transcription factor binding sites in them. These binding sites can provide explanations for the co-expressed patterns.

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Standardization of Gene Expression Quantification by Absolute Real-Time qRT-PCR System Using a Single Standard for Marker and Reference Genes

Biomarker Insights ◽

10.4137/bmi.s5596 ◽

2010 ◽

Vol 5 ◽

pp. BMI.S5596 ◽

Cited By ~ 7

Author(s):

Yi-Hong Zhou ◽

Vinay R. Raj ◽

Eric Siegel ◽

Liping Yu

Keyword(s):

Gene Expression ◽

Real Time ◽

Gene Expression Data ◽

Reference Genes ◽

Pcr Analysis ◽

Expression Data ◽

Internal Reference ◽

Expression Ratio ◽

Qrt Pcr ◽

Single Standard

In the last decade, genome-wide gene expression data has been collected from a large number of cancer specimens. In many studies utilizing either microarray-based or knowledge-based gene expression profiling, both the validation of candidate genes and the identification and inclusion of biomarkers in prognosis-modeling has employed real-time quantitative PCR on reverse transcribed mRNA (qRT-PCR) because of its inherent sensitivity and quantitative nature. In qRT-PCR data analysis, an internal reference gene is used to normalize the variation in input sample quantity. The relative quantification method used in current real-time qRT-PCR analysis fails to ensure data comparability pivotal in identification of prognostic biomarkers. By employing an absolute qRT-PCR system that uses a single standard for marker and reference genes (SSMR) to achieve absolute quantification, we showed that the normalized gene expression data is comparable and independent of variations in the quantities of sample as well as the standard used for generating standard curves. We compared two sets of normalized gene expression data with same histological diagnosis of brain tumor from two labs using relative and absolute real-time qRT-PCR. Base-10 logarithms of the gene expression ratio relative to ACTB were evaluated for statistical equivalence between tumors processed by two different labs. The results showed an approximate comparability for normalized gene expression quantified using a SSMR-based qRT-PCR. Incomparable results were seen for the gene expression data using relative real-time qRT-PCR, due to inequality in molar concentration of two standards for marker and reference genes. Overall results show that SSMR-based real-time qRT-PCR ensures comparability of gene expression data much needed in establishment of prognostic/predictive models for cancer patients–-a process that requires large sample sizes by combining independent sets of data.

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The comprehensive transcriptional analysis in Caenorhabditis elegans by integrating ChIP-seq and gene expression data

Genetics Research ◽

10.1017/s0016672314000081 ◽

2014 ◽

Vol 96 ◽

Cited By ~ 2

Author(s):

KAN HE ◽

JIAOFANG SHAO ◽

ZHONGYING ZHAO ◽

DAHAI LIU

Keyword(s):

Gene Expression ◽

Transcriptional Regulation ◽

Caenorhabditis Elegans ◽

Gene Expression Data ◽

Target Genes ◽

Model Systems ◽

Transcriptional Analysis ◽

Expression Data ◽

Tissue Specific ◽

C Elegans

SummaryThe fundamental step of learning transcriptional regulation mechanism is to identify the target genes regulated by transcription factors (TFs). Despite numerous target genes identified by chromatin immunopre-cipitation followed by high-throughput sequencing technology (ChIP-seq) assays, it is not possible to infer function from binding alone in vivo. This is equally true in one of the best model systems, the nematode Caenorhabditis elegans (C. elegans), where regulation often occurs through diverse TF binding features of transcriptional networks identified in modENCODE. Here, we integrated ten ChIP-seq datasets with genome-wide expression data derived from tiling arrays, involved in six TFs (HLH-1, ELT-3, PQM-1, SKN-1, CEH-14 and LIN-11) with tissue-specific and four TFs (CEH-30, LIN-13, LIN-15B and MEP-1) with broad expression patterns. In common, TF bindings within 3 kb upstream of or within its target gene for these ten studies showed significantly elevated level of expression as opposed to that of non-target controls, indicated that these sites may be more likely to be functional through up-regulating its target genes. Intriguingly, expression of the target genes out of 5 kb upstream of their transcription start site also showed high levels, which was consistent with the results of following network component analysis. Our study has identified similar transcriptional regulation mechanisms of tissue-specific or broad expression TFs in C. elegans using ChIP-seq and gene expression data. It may also provide a novel insight into the mechanism of transcriptional regulation not only for simple organisms but also for more complex species.

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Model of Tryptophan Metabolism, Readily Scalable Using Tissue-specific Gene Expression Data

Journal of Biological Chemistry ◽

10.1074/jbc.m113.474908 ◽

2013 ◽

Vol 288 (48) ◽

pp. 34555-34566 ◽

Cited By ~ 27

Author(s):

Anne-Kristin Stavrum ◽

Ines Heiland ◽

Stefan Schuster ◽

Pål Puntervoll ◽

Mathias Ziegler

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Tryptophan Metabolism ◽

Specific Gene ◽

Expression Data ◽

Tissue Specific ◽

Tissue Specific Gene ◽

Specific Gene Expression

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Identification and Validation of Reference Genes for the Normalization of Gene Expression Data in qRT-PCR Analysis inAphis gossypii(Hemiptera: Aphididae)

Journal of Insect Science ◽

10.1093/jisesa/iew003 ◽

2016 ◽

Vol 16 (1) ◽

pp. 17 ◽

Cited By ~ 42

Author(s):

Kang-Sheng Ma ◽

Fen Li ◽

Ping-Zhuo Liang ◽

Xue-Wei Chen ◽

Ying Liu ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Reference Genes ◽

Pcr Analysis ◽

Expression Data ◽

Qrt Pcr

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Analysis of High Dimensionality Yeast Gene Expression Data Using Data Mining

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.197.515 ◽

2012 ◽

Vol 197 ◽

pp. 515-522

Author(s):

Mazin Aouf ◽

Liwan Liyanage

Keyword(s):

Gene Expression ◽

Data Mining ◽

Hydrogen Peroxide ◽

Transcription Factors ◽

Gene Expression Data ◽

Process Model ◽

Expression Data ◽

Yeast Gene ◽

Promoter Regions ◽

Biological Studies

Data Mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. From biological studies, the Yeast Proteome Database (YPD) is a model for the organization and presentation of genome-wide functional data. Accordingly, a yeast gene expression which is a unicellular DNA is selected which contains 6103 genes and the database combined with a number of related dataset to create a general dataset. DNA-binding transcriptional regulators interpret the genome’s regulatory code by binding to specific sequences to induce or repress gene expression. The gene products including RNA and protein are responsible for the development and functioning of all living membranes by 2 steps process, transcription and translation. Various transcription factors control gene transcription by binding to the promoter regions. Translation is the production of proteins from mRNA produced in transcription. In this study, out of the 169 transcription factors known to access yeast, we are considering those thought to be involved in the response of Hydrogen Peroxide (H2O2). They are 22 transcription factors. Each one is partitioned to 3 parts: TF with No H2O2, TF with Low H2O2 and TF with High H2O2. The aim of this paper was to enhance the effectiveness of the integration of hydrogen peroxide response data related to yeast gene expression data to obtain a protein response process model and to label a set of important genes related to this approach.

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